Limited Communication

Limited communication in distributed systems is a significant challenge across diverse fields, driving research into efficient algorithms and architectures that minimize data exchange while maintaining performance. Current efforts focus on developing novel optimization techniques, such as adaptive federated learning and consensus-based methods, often incorporating strategies like subgraph sampling or lazy communication to reduce transmission overhead. These advancements are crucial for enabling scalable machine learning, efficient multi-agent coordination in robotics and autonomous systems, and improved performance in resource-constrained environments like wireless networks. The resulting improvements in communication efficiency have broad implications for various applications, ranging from training large language models to cooperative autonomous driving.